1. Field of the Invention
This application presents direct data recovery (DDR) based on reversing transmission channel transfer function, in order to achieve a direct recovery of original data and synchronizing clock from received signals affected by all deterministic and random distortions introduced by the channel.
The DDR can eliminate an intermediate recovery of signal transmitted originally from received signal, required in conventional solutions before actual data recovery can be made.
Therefore DDR can prevent signal processing errors added by such intermediate recovery and reduce power consumption and computing resources required in conventional receivers.
The DDR is applicable to communication channels including Non Return Zero (NRZ) or Pulse Amplitude Modulation (PAM), OFDM Multi-carrier/Multi-tone, Carrierless Amplitude Phase (CAP), Frequency Modulation (FM), Phase Modulation (PM).
The DDR can be applied in data recovery systems for wireless, optical, or wireline communication and in local or remote measurement systems.
The DDR shall be particularly advantageous in system on chip (SOC) implementations of data recovery systems.
Such DDR includes utilization of inverse signal transformation (IST) presented in the parent application as comprising a noise filtering with inverse transformation (NFIT) and phase and frequency recovery techniques (PFRT) described therein by separate subsections taken from their application Ser. No. 12/047,318 and Ser. No. 11/931,026 accordingly.
Some elements of asynchronous data recovery (ADR) solutions presented earlier by the same applicant in PCT/CA06/001332, can be useful in explaining a background field to DDR contributions in PAM and coherent optical communication.
2. Background of DDR
2.1 General Background of DDR and IST
Conventional methods and systems for data recovery are directed to transformation of specific received signals into shapes similar to those transmitted originally before any decoding of data,    as they use fixed data decoding schemes, applicable only to such similarly shaped signals, in order to decode data encoded originally in the transmitter.
Such conventional solutions, focused on recovering original signal shapes from specific received signal shapes, can not be effective in reversing dynamic and random signal distortions introduced by data links, since:    said distortions are projecting said original signal shapes into received signal subspaces instead of transforming them into said specific received signals;    said conventional solutions are not directed to applying varying data decoding schemes responding to said transfer function of transmission channel and current characteristics of received signal.
In conditions of constantly growing data rates, data links complexity and spectrum utilization, distortions introduced by transmission channels are growing into major parts of signals received from remote sources in electronic environments contaminated highly.
Therefore the conventional methods based on said recovery of original signal required by said fixed decoding, become comparable to chasing a butterfly into a route leading it into a fixed net instead of letting butterfly to fly freely and moving the net into its path.
The IST is based on a fundamentally different principle of operation than such conventional systems, because mobile adaptive decoding is applied directly to said received signal space, distorted by the transmission channel, instead of applying such fixed decoding to the original signal recovered from said received signal space.
Such IST includes utilizing a relation between a subset of received signal space (comprising a particular received signal) and data encoded originally into this signal, wherein such relation includes said inverse transformation of channel function.
Furthermore said conventional data recovery from received signal requires complex processing for achieving said recovery of original data carrying signal, wherein such complex processing is applied continuously to a waveform of over-sampled received signal.
IST replaces such complex processing of received signal with a direct application of reference frames to the received signal waveform,    wherein said reference frames, representing expected shapes of received signal intervals, are compared with received signal shapes in order to identify original signal shapes which these received signal shapes correspond to.
Still furthermore said reference frames and/or their parameters can be derived by a background processing responding to changes of transmission channel which are by many orders slower than changes of transmitted signal,    while said recovery of original signal shape requires a real time processing responding to the changes of transmitted signal which are by many orders faster.
Therefore such conventional solutions, spending resources on such “real time reconstruction” of very fast original signals instead of focusing on said more direct data recovery of original data from said received signal subspaces, can not be efficient in utilizing processing resources or minimizing power.
Consequently, conventional data recovery methods and circuits have limitations causing that only linear time invariant filters (LTI filters) can be used in majority of serial communication links.
Such LTI approximations impair filtering efficiency of the majority of the communication links which are non-linear and time variant and have changing in time characteristics.
Furthermore due to such limitations of conventional solutions; even rarely used non-linear and/or adaptive filters using adaptive algorithms to accommodate changing in time characteristics of transmission channels, can accommodate only limited and slowly changing portions of signal non-linearity and/or distortion caused by nonlinear and/or changing in time characteristics of transmission channel.
It is the objective of DDR to alleviate such limitations by enabling more efficient accommodation of line-load, non-linearity and time variant quick changes of transmission channel such as those caused by cross-talk and inter-band interference from adjacent transmission channels.
Non-provisional patent application U.S. Ser. No. 11/931,026 by Bogdan introduced utilization of reference frames for detecting data carrying intervals of received signals named therein as received signal edges.
Later than this 931026, PCT/CA06/001332 by Bogdan (see WO 2007/009266), disclosed improved utilization of such edge detection techniques including a comparison of said received signal with edge masks selected adaptively. Similar tools can be also utilized in next inventions such as DDR and ADD presented herein.
However the 001332 still requires said recovery of original data carrying signal or its data defining parameters which involves more complex processing and is much less efficient in reversing distortions and interferences introduced by the transmission channel.
Therefore DDR contributes the fundamentally different principle of operation explained above and further below, in order to enable major improvements in signal processing efficiency and accuracy over those enabled by the earlier 001332 and the other conventional solutions.
Most of earlier data recovery systems; require phase locking to the original transmitter's clock recovered from the distorted received signal. Such recovery of original clock has to be preceded by recovering an original shape of received signal, in order to minimize phase locking errors caused by signal distortions. Therefore such earlier systems implement frequency domain filters for noise reduction in the received waveform, and compensate line loads with a feedback signal connected from a receivers output to an input of the receiver.
Said phase locking eliminates immunity to high frequency phase noise exceeding bandwidth of receivers PLL.
Said frequency domain filters are inefficient in responding to changing high frequency noise and often attenuate high frequency data, while conventional line load compensation offers only delayed responses involving feedback signals which may compromise accuracy and/or stability of line receivers.
In particular, said frequency domain filters are conventionally used for recovering shape of original signal from serially transmitted pulses.
Since serially transmitted pulses must have widely variable lengths and frequencies, such frequency domain filters can not eliminate high frequency phase jitter and attenuate useful part of signal while filtering high frequency noise.
Consequently such-frequency domain filters are inherently inefficient and inaccurate in detecting phase of data carrying signals; while accurate and reliable phase detection is becoming essential for efficient modern communication based on NRZ/PAM, or PM over copper/fiber/wireless links.
Since such modern communication links utilize phases of signal transitions between limited set of signal levels or amplitudes for data encoding, said limitations in phase detection accuracy and noise filtering abilities reduce data rates and/or link lengths.
These earlier systems' limitations were partly addressed by solutions presented in the 001332, wherein:    a received signal is densely over-sampled and phases and amplitudes of data carrying pulses and phases of their edges are recovered without causing any signal attenuation;    and a number of data symbols contained in the pulse is determined by measuring length of such pulse instead of relying on sampling pulse amplitude with a phase aligned clock targeting a middle of symbol time periods.
In addition to the elimination of said phase alignment of a local receiver clock, the 001332 presents solutions directed to instant compensation of line load effects and crosstalk noise.
Nevertheless even the 001332, still requires said recovery of original data carrying signal or its data defining parameters.
Therefore it still has limited efficiency in inverting signal distortions introduced by data links, as they can not apply said direct data decoding in order to enable accurate and timely responses to said fast changes of the data carrying signals of high speed communication links.
2.2 Background of NFIT
The purpose of noise filters is to reconstruct original signal by reduction of received signal components representing noise and/or by enhancement of received signal components representing the original signal.
Limitations of conventional noise filtering methods and electronic circuit technologies cause that only linear time invariant filters (LTI filters) can be used in majority of serial communication links.
Such LTI approximations impair filtering efficiency of the majority of the communication links which are non-linear and time variant and have changing in time characteristics.
Furthermore due to such limitations of conventional solutions; even rarely used non-linear and/or adaptive filters using adaptive algorithms to accommodate changing in time characteristics of transmission channels, can accommodate only limited and slowly changing portions of signal non-linearity and/or distortion caused by nonlinear and/or changing in time characteristics of transmission channel.
Frequency sampling filters (FSF) capable of recovering particular sinusoidal tones/sub-bands from a composite signal such as OFDM frame, were known and described as rarely used in the book by Richard G. Lyons; “Understanding Digital Signal Processing”, Second Edition 2004 Prentice Hall.
However such frequency sampling filters and other conventional frequency domain methods do not have time domain solutions needed to preserve and recover phase alignments of singular cycles of tones/sub-bands to the composite signal frame, wherein such phase alignments carry phases of tones/sub-bands transmitting data encoded originally.
It is the objective of solutions presented herein to alleviate such limitations by contributing;    accommodation of unlimited non-linearity and time variant quick changes of transmission channel such as those caused by line load, cross-talk and inter-band interference from adjacent transmission channels,    and said time domain solutions combining signal processing in frequency domain and in time domain, in order to enable recovery of phases and amplitudes of singular cycles or half-cycles of data carrying tones or sub-bands comprised in the composite signal.